Train-time techniques aim to combat fairness bias during model training. This can be accomplished using constraints based on adversarial loss [1]}, [2]}, [3]}, feature importance [4]}, [5]}, [6]}, fairness measurement [7]}, decision boundary [8]} or statistical dependence [9]}. Adversarial loss requires defining additional heads or constraints for a specific protected attribute. It maximizes the primary objective of a specific task while minimizing the model's ability to predict specific protected attributes. Constraints based on feature importance, on the other hand, heavily rely on the feature contribution score [4]}, [5]}, which is not always reliable [12]}.
 [13]} propose Bayes-optimal classification framework for intersectional group fairness which is also tied to fairness metric constraints.
 [14]} propose a framework based on mutual information minimization for intersectional subgroups. This framework, however, requires two estimators and two additional predictors which makes the mitigation process complex.
